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Marcellus Shale Lithofacies Prediction by Multiclass Neural Network Classification in the Appalachian Basin

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Abstract

Marcellus Shale is a rapidly emerging shale-gas play in the Appalachian basin. An important component for successful shale-gas reservoir characterization is to determine lithofacies that are amenable to hydraulic fracture stimulation and contain significant organic-matter and gas concentration. Instead of using petrographic information and sedimentary structures, Marcellus Shale lithofacies are defined based on mineral composition and organic-matter richness using core and advanced pulsed neutron spectroscopy (PNS) logs, and developed artificial neural network (ANN) models to predict shale lithofacies with conventional logs across the Appalachian basin. As a multiclass classification problem, we employed decomposition technology of one-versus-the-rest in a single ANN and pairwise comparison method in a modular approach. The single ANN classifier is more suitable when the available sample number in the training dataset is small, while the modular ANN classifier performs better for larger datasets. The effectiveness of six widely used learning algorithms in training ANN (four gradient-based methods and two intelligent algorithms) is compared with results indicating that scaled conjugate gradient algorithms performs best for both single ANN and modular ANN classifiers. In place of using principal component analysis and stepwise discriminant analysis to determine inputs, eight variables based on typical approaches to petrophysical analysis of the conventional logs in unconventional reservoirs are derived. In order to reduce misclassification between widely different lithofacies (for example organic siliceous shale and gray mudstone), the error efficiency matrix (ERRE) is introduced to ANN during training and classification stage. The predicted shale lithofacies provides an opportunity to build a three-dimensional shale lithofacies model in sedimentary basins using an abundance of conventional wireline logs. Combined with reservoir pressure, maturity and natural fracture system, the three-dimensional shale lithofacies model is helpful for designing strategies for horizontal drilling and hydraulic fracture stimulation.

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Acknowledgements

This research was supported by the U.S. Department of Energy National Energy Technology Laboratory’s Regional University Alliance (NETL-RUA) under the contract RES1000023/155 (Activity 4.605.920.007) and National Natural Science Foundation of China (No. 698796867). Special thanks to Energy Corporation of America, Consol Energy, EQT Production and Petroleum Develop Corporation for providing core and log data.

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Authors and Affiliations

  1. Department of Geology & Geography, West Virginia University, 98 Beechurst Ave, 330 Brooks Hall, P.O. Box 6300, Morgantown, WV, 26506-6300, USA

    Guochang Wang & Timothy R. Carr

  2. Petroleum Engineering Department, China University of Geosciences (Wuhan), Wuhan, Hubei, 430074, China

    Guochang Wang

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  1. Guochang Wang
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  2. Timothy R. Carr
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Correspondence to Guochang Wang.

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Wang, G., Carr, T.R. Marcellus Shale Lithofacies Prediction by Multiclass Neural Network Classification in the Appalachian Basin. Math Geosci 44, 975–1004 (2012). https://doi.org/10.1007/s11004-012-9421-6

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  • DOI: https://doi.org/10.1007/s11004-012-9421-6

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